{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    2\n",
       "2    5\n",
       "3    0\n",
       "4    6\n",
       "5    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "s = pd.Series([4, 2, 5, 0, 6, 3])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.174876</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>-0.236220</td>\n",
       "      <td>-0.828610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.719138</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>-0.156899</td>\n",
       "      <td>-2.356175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.029569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.550851</td>\n",
       "      <td>-0.027446</td>\n",
       "      <td>-0.015270</td>\n",
       "      <td>-0.759147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.809620</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>-0.391964</td>\n",
       "      <td>-0.835977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>-0.613681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.174876 -1.267868 -0.236220 -0.828610\n",
       "1 -0.719138  1.299475 -0.156899 -2.356175\n",
       "2  1.012941  0.636643  0.006203  0.029569\n",
       "3 -0.550851 -0.027446 -0.015270 -0.759147\n",
       "4 -0.809620  0.177089 -0.391964 -0.835977\n",
       "5  0.890224 -0.325515  0.748274 -0.613681"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 4), columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.174876\n",
       "B   -1.267868\n",
       "C   -0.236220\n",
       "D   -0.828610\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.174876\n",
       "1   -0.719138\n",
       "2    1.012941\n",
       "3   -0.550851\n",
       "4   -0.809620\n",
       "5    0.890224\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row data type: <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Row data type: {}\".format(type(df.iloc[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column data type: <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Column data type: {}\".format(type(df.A)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.174876</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>-0.236220</td>\n",
       "      <td>-0.828610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.719138</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>-0.156899</td>\n",
       "      <td>-2.356175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.029569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.174876 -1.267868 -0.236220 -0.828610\n",
       "1 -0.719138  1.299475 -0.156899 -2.356175\n",
       "2  1.012941  0.636643  0.006203  0.029569"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.809620</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>-0.391964</td>\n",
       "      <td>-0.835977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>-0.613681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "4 -0.809620  0.177089 -0.391964 -0.835977\n",
       "5  0.890224 -0.325515  0.748274 -0.613681"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-0.000261</td>\n",
       "      <td>0.082063</td>\n",
       "      <td>-0.007646</td>\n",
       "      <td>-0.894003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.815501</td>\n",
       "      <td>0.871677</td>\n",
       "      <td>0.398469</td>\n",
       "      <td>0.786809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-0.809620</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>-0.391964</td>\n",
       "      <td>-2.356175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.677066</td>\n",
       "      <td>-0.250997</td>\n",
       "      <td>-0.216390</td>\n",
       "      <td>-0.834135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-0.187987</td>\n",
       "      <td>0.074822</td>\n",
       "      <td>-0.086084</td>\n",
       "      <td>-0.793879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.711387</td>\n",
       "      <td>0.521755</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>-0.650047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>0.029569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.000261  0.082063 -0.007646 -0.894003\n",
       "std    0.815501  0.871677  0.398469  0.786809\n",
       "min   -0.809620 -1.267868 -0.391964 -2.356175\n",
       "25%   -0.677066 -0.250997 -0.216390 -0.834135\n",
       "50%   -0.187987  0.074822 -0.086084 -0.793879\n",
       "75%    0.711387  0.521755  0.000835 -0.650047\n",
       "max    1.012941  1.299475  0.748274  0.029569"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.828610</td>\n",
       "      <td>-0.236220</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>0.174876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-2.356175</td>\n",
       "      <td>-0.156899</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>-0.719138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.029569</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>1.012941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.759147</td>\n",
       "      <td>-0.015270</td>\n",
       "      <td>-0.027446</td>\n",
       "      <td>-0.550851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.835977</td>\n",
       "      <td>-0.391964</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>-0.809620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-0.613681</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>0.890224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          D         C         B         A\n",
       "0 -0.828610 -0.236220 -1.267868  0.174876\n",
       "1 -2.356175 -0.156899  1.299475 -0.719138\n",
       "2  0.029569  0.006203  0.636643  1.012941\n",
       "3 -0.759147 -0.015270 -0.027446 -0.550851\n",
       "4 -0.835977 -0.391964  0.177089 -0.809620\n",
       "5 -0.613681  0.748274 -0.325515  0.890224"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.174876</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>-0.236220</td>\n",
       "      <td>-0.828610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>-0.613681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.550851</td>\n",
       "      <td>-0.027446</td>\n",
       "      <td>-0.015270</td>\n",
       "      <td>-0.759147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.809620</td>\n",
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       "      <td>-0.835977</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.029569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.719138</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>-0.156899</td>\n",
       "      <td>-2.356175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.174876 -1.267868 -0.236220 -0.828610\n",
       "5  0.890224 -0.325515  0.748274 -0.613681\n",
       "3 -0.550851 -0.027446 -0.015270 -0.759147\n",
       "4 -0.809620  0.177089 -0.391964 -0.835977\n",
       "2  1.012941  0.636643  0.006203  0.029569\n",
       "1 -0.719138  1.299475 -0.156899 -2.356175"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          A         B         C         D\n",
       "3 -0.550851 -0.027446 -0.015270 -0.759147\n",
       "4 -0.809620  0.177089 -0.391964 -0.835977"
      ]
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.177089</td>\n",
       "      <td>-0.835977</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>-0.613681</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "          A         B         D\n",
       "0  0.174876 -1.267868 -0.828610\n",
       "1 -0.719138  1.299475 -2.356175\n",
       "2  1.012941  0.636643  0.029569\n",
       "3 -0.550851 -0.027446 -0.759147\n",
       "4 -0.809620  0.177089 -0.835977\n",
       "5  0.890224 -0.325515 -0.613681"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A', 'B', 'D']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5508506258624778"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3, 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5508506258624778"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <td>4</td>\n",
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       "          A         B\n",
       "2  1.012941  0.636643\n",
       "3 -0.550851 -0.027446\n",
       "4 -0.809620  0.177089"
      ]
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     "execution_count": 26,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[2:5, 0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.029569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "2  1.012941  0.636643  0.006203  0.029569\n",
       "5  0.890224 -0.325515  0.748274 -0.613681"
      ]
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     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "df[df.C > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TAG'] = [\"cat\", \"dog\", \"cat\", \"cat\", \"cat\", \"dog\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>TAG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.174876</td>\n",
       "      <td>-1.267868</td>\n",
       "      <td>-0.236220</td>\n",
       "      <td>-0.828610</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.719138</td>\n",
       "      <td>1.299475</td>\n",
       "      <td>-0.156899</td>\n",
       "      <td>-2.356175</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.012941</td>\n",
       "      <td>0.636643</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.029569</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.550851</td>\n",
       "      <td>-0.027446</td>\n",
       "      <td>-0.015270</td>\n",
       "      <td>-0.759147</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.809620</td>\n",
       "      <td>0.177089</td>\n",
       "      <td>-0.391964</td>\n",
       "      <td>-0.835977</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.890224</td>\n",
       "      <td>-0.325515</td>\n",
       "      <td>0.748274</td>\n",
       "      <td>-0.613681</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D  TAG\n",
       "0  0.174876 -1.267868 -0.236220 -0.828610  cat\n",
       "1 -0.719138  1.299475 -0.156899 -2.356175  dog\n",
       "2  1.012941  0.636643  0.006203  0.029569  cat\n",
       "3 -0.550851 -0.027446 -0.015270 -0.759147  cat\n",
       "4 -0.809620  0.177089 -0.391964 -0.835977  cat\n",
       "5  0.890224 -0.325515  0.748274 -0.613681  dog"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAG</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>cat</td>\n",
       "      <td>-0.172653</td>\n",
       "      <td>-0.481582</td>\n",
       "      <td>-0.637251</td>\n",
       "      <td>-2.394165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dog</td>\n",
       "      <td>0.171086</td>\n",
       "      <td>0.973960</td>\n",
       "      <td>0.591375</td>\n",
       "      <td>-2.969855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D\n",
       "TAG                                        \n",
       "cat -0.172653 -0.481582 -0.637251 -2.394165\n",
       "dog  0.171086  0.973960  0.591375 -2.969855"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('TAG').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(366,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.338627\n",
       "2000-01-02   -1.001356\n",
       "2000-01-03    2.896816\n",
       "2000-01-04    0.027891\n",
       "2000-01-05    0.923120\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_items = 366\n",
    "ts = pd.Series(np.random.randn(n_items), index=pd.date_range('20000101', periods=n_items))\n",
    "\n",
    "print(ts.shape)\n",
    "ts.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    -0.572236\n",
       "2000-02-29    11.803245\n",
       "2000-03-31    -2.556009\n",
       "2000-04-30     3.044104\n",
       "2000-05-31    -4.524522\n",
       "2000-06-30     1.132713\n",
       "2000-07-31     1.736739\n",
       "2000-08-31   -10.535715\n",
       "2000-09-30     5.833979\n",
       "2000-10-31     0.639357\n",
       "2000-11-30     1.399501\n",
       "2000-12-31     4.464057\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"1m\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126438e4248>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6), dpi=144)\n",
    "cs = ts.cumsum()\n",
    "cs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1264402fb88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=144)\n",
    "ts.resample(\"1m\").sum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv', index_col=0)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.845734</td>\n",
       "      <td>1.359361</td>\n",
       "      <td>1.148205</td>\n",
       "      <td>-1.021024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-2.014522</td>\n",
       "      <td>-0.045063</td>\n",
       "      <td>1.329018</td>\n",
       "      <td>-0.176059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.411952</td>\n",
       "      <td>-0.652460</td>\n",
       "      <td>-2.277084</td>\n",
       "      <td>-0.648005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.179014</td>\n",
       "      <td>-0.218886</td>\n",
       "      <td>-0.629854</td>\n",
       "      <td>-0.474365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.523180</td>\n",
       "      <td>-0.904850</td>\n",
       "      <td>0.788943</td>\n",
       "      <td>-0.095711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.845734  1.359361  1.148205 -1.021024\n",
       "1 -2.014522 -0.045063  1.329018 -0.176059\n",
       "2 -0.411952 -0.652460 -2.277084 -0.648005\n",
       "3  1.179014 -0.218886 -0.629854 -0.474365\n",
       "4  1.523180 -0.904850  0.788943 -0.095711"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
